# Literature Review Matrix for Quantitative Research: Template and Examples By the end of this article you will be able to build a literature review matrix for [quantitative research](/blog/guides/quantitative-research), fill it with studies from a systematic literature search, and turn the finished matrix into the synthesis sections of a written review. You need three things on hand: a spreadsheet program such as Excel, Google Sheets, or LibreOffice Calc, a reference manager such as Zotero or EndNote for storing PDFs, and a written research question. Everything else in this guide uses free tools. ## What a Literature Review Matrix Is A literature review matrix is a table in which each row is one source and each column is a standardized piece of information about that source, such as design, sample, outcome measures, effect sizes, and quality rating. Instead of reading thirty papers and remembering thirty separate impressions, you read them once, extract the same fields from each, and compare studies side by side. The matrix is a working tool, not a final product. Research teams use exactly this structure during formal evidence synthesis. Birken and colleagues abstracted data from 105 records into a matrix for analysis, then summarized categorical variables with descriptive statistics before coding qualitative content [1]. A scoping review of integrated health campaigns extracted data "following a predefined matrix" and then analyzed it deductively [2]. The practice has a long record in published research, which means you can borrow its conventions with confidence. ## Why Quantitative Research Needs a Matrix Quantitative studies report numbers, and numbers resist fuzzy memory. A single paragraph of prose cannot hold the sample size, effect measure, direction of effect, and risk-of-bias judgment for twenty studies at once. A matrix can, because each of those items lives in its own column where you can sort and filter it. The matrix also disciplines your synthesis. When reviews of knee cartilage repair could not harmonize effect measures across 31 reports, the authors organized their synthesis by disease context, procedure, factor, outcome, and follow-up, which is precisely the kind of categorization a matrix gives you for free [3]. When a review of brain organoid vascularization needed to evaluate evidence across engineering strategies, the authors built their Multidimensional Quality Assessment Framework from quantitative data in 13 published studies [4]. Both teams needed a structured tabular view of the literature before they could synthesize anything. ## The Core Format: Columns That Matter There is no single mandated literature review matrix format, but published reviews converge on a common core. The table below summarizes the columns used most often in quantitative syntheses. | Column | What it holds | Example entry | |--|--|--| | Citation | Author, year, short identifier | Amaral 2026, CRC trace elements | | Study design | RCT, cohort, case-control, cross-sectional, systematic review | Systematic review and meta-analysis | | Question or PICO element | Population, exposure, comparator | Adults with colorectal cancer vs healthy controls | | Sample | N, species, setting, dates | 19 human studies, 1994 to 2026 | | Exposure or intervention | What was measured or done | Circulating trace elements via ICP-MS | | Outcomes | What was measured as the result | Blood selenium, copper, lead, arsenic | | Effect measure and estimate | Odds ratio, SMD, mean difference with CI | Selenium SMD -0.74 | | [Statistical methods](/blog/guides/statistical-methods) | Model used, software if reported | Random-effects meta-analysis | | Quality or risk of bias | Tool name and score | Newcastle-Ottawa Scale | | Key finding in one sentence | Your paraphrase, not a quote | Systemic selenium depletion in CRC | | Notes | Limitations, gaps, follow-up ideas | No longitudinal data | Two design choices matter more than the exact column list. **One row per effect, not one row per paper.** A single study often reports several outcomes. If a paper reports three effect estimates, give it three rows that share a citation ID. This is how association-level extraction works in prognosis research, where reviewers extracted "association-level data" for each factor-outcome pair rather than one blob per paper [3]. **Separate description from judgment.** Keep columns such as "reported result" strictly factual, and put your evaluation in a clearly labeled quality column. Mixing them makes the matrix unusable later. ## Building Your Template Step by Step ![Six step flow from writing the PICO or PECO question through searching, screening, setting up the spreadsheet, two-pass extraction and synthesis.](/article-images/illustrations/literature-review-matrix-quantitative-research-template-build-steps.webp) ### Step 1. Write the question in PICO or PECO form Quantitative reviews start from a structured question. For intervention questions use PICO (Population, Intervention, Comparison, Outcome). For exposure and etiology questions use PECO, which substitutes Exposure for Intervention. A published meta-analysis of trace elements in colorectal cancer followed PRISMA guidelines with a PECO framework, defining eligible studies as original human research using ICP-MS or ICP-OES to measure trace elements in colorectal cancer biological matrices compared with healthy controls [5]. That single sentence of protocol did three jobs at once: it set the population, the exposure of interest, and the comparator. Write your version in one sentence before you touch a database. Every later decision, including which columns your matrix needs, flows from it. ### Step 2. Run a systematic literature search A systematic literature search is documented, multi-database, and date-stamped. The pattern across published reviews is consistent. 1. Choose 3 or 4 major databases for your field. Medline, Embase, and Scopus are a common triple in health research [6]. PubMed, Web of Science, and Scopus serve biomedical and analytical work [5]. PubMed, Scopus, and Google Scholar appear in reviews that also harvest grey literature from institutional websites [2]. 2. Combine keyword and controlled-vocabulary terms. A review of heat shock proteins in gastric cancer combined keywords and MeSH terms across three domains, the protein family, the disease, and therapeutics [7]. 3. Record the exact date range. One scoping review searched from January 2009 to July 2025 [6]. A tissue-banking review anchored its search to October 15, 2025 [8]. Write your dates down the same day you search. 4. Add citation chasing. Backward citation searching means checking the reference lists of included papers, and forward citation searching means finding newer papers that cite them. The knee cartilage review used both [3]. Save every search string and every database name in a plain text file. You will need them for your methods section, and reviewers of your manuscript will ask. ### Step 3. Screen records and count everything Screening moves from titles to abstracts to full texts, and each stage discards records. The numbers you generate here feed the flow diagram required by PRISMA, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The scale is normal. A systematic review of residual DNA quantification in dermal grafts screened 1,250 records and included 10 studies [8]. A PRISMA-guided review of nanomaterials in fish wound healing included 12 in vivo studies from 2017 to 2026 [9]. Losing 99 percent of your search results at screening is a feature of a well-targeted question, not a failure. Keep a simple screening log in your spreadsheet with columns for record ID, title, decision (include, exclude, maybe), and exclusion reason. Consistent reasons at full text become a table in your paper later. ### Step 4. Set up the matrix spreadsheet Create a new sheet and paste this header row into cell A1. ``` ID,Citation,Year,Design,Population,Sample N,Exposure or Intervention,Comparator,Outcomes,Effect measure,Effect estimate (95% CI),Statistical model,Quality tool,Quality result,Key finding (own words),Notes S001,Amaral 2026,2026,Syst review + meta-analysis,Adults with CRC,19 studies,Trace elements (ICP-MS/ICP-OES),Healthy controls,Blood Se Cu Pb As + tumor tissue,Standardized mean difference,Se SMD -0.74,Random-effects,Newcastle-Ottawa Scale,Reported,Se markedly depleted in circulation,See also Cu Pb As rows S001b-S001d ``` If you save that as a CSV and open it, you get a working matrix with one filled example row. For a quick sanity check on data entry, count how many rows report a specific design. ``` =COUNTIF(D2:D200,"cross-sectional") ``` Expected output: a single number, for example 14, telling you how many of your extracted studies are cross-sectional. Sorting and filtering on the Design, Effect measure, and Quality result columns is where the matrix starts paying for itself. ### Step 5. Fill rows using a two-pass extraction Extract on the first pass for facts and the second pass for meaning. **Pass one, facts.** For each included paper, copy only what the authors actually report into the descriptive columns. If a paper reports that nanoparticle treatments in farmed fish produced faster wound closure with several formulations achieving over 90 percent closure within two weeks, that sentence belongs in the effect column with its citation [9]. Do not round, convert, or reinterpret while extracting. **Pass two, meaning.** In the "Key finding" column, write one paraphrased sentence per row in your own words, focused on direction and magnitude. Reserve the Notes column for limitations the authors state, such as the wound-healing authors' caveat that their quantitative outcomes were strictly descriptive because of high methodological heterogeneity across studies [9]. Two published matrices show what full extraction looks like at scale. The review of trace elements in colorectal cancer extracted data from 19 studies published between 1994 and 2026 and ran random-effects meta-analyses producing pooled standardized mean differences, including a circulating selenium SMD of -0.74 with a 95 percent confidence interval, alongside elevated copper, lead, and arsenic values [5]. The middle-manager review converted its matrix into descriptive statistics for categorical variables and a priori codes for [qualitative data](/blog/guides/qualitative-data-analysis-a-practical-guide-for-life-scientists) [1]. Both workflows began as the same spreadsheet you just built. ### Step 6. Synthesize across rows Synthesis is reading down the columns, not across the rows. Four moves cover most quantitative reviews. 1. **Count and describe.** How many studies, what designs, what years, what settings. Descriptive statistics over the matrix answers this [1]. 2. **Compare estimates.** When studies report compatible effect measures, group them. A meta-analysis pools them formally with random-effects models and reports pooled standardized mean differences with [confidence intervals](/blog/data-analysis/confidence-interval-formula-examples) [5]. 3. **Split when measures conflict.** When effect measures cannot be harmonized, synthesize narratively by grouping rows on context, procedure, factor, and outcome, exactly as the cartilage review did across 31 reports [3]. 4. **Derive frameworks.** Some of the most useful outputs come from pattern-spotting in the matrix. The organoid vascularization review turned 13 studies into a named multidimensional assessment framework [4]. A biomonitoring review integrated monitoring duration, quantitative accuracy, and ethical constraints into a [decision tree](/blog/data-analysis/what-is-a-decision-tree) for choosing a sample matrix [10]. If your columns repeatedly interact, a framework or decision tree is a legitimate synthesis product. ## Adapting the Matrix to Your Review Type ![Four-column comparison of meta-analysis, narrative synthesis, scoping and realist reviews showing which matrix columns change for each.](/article-images/illustrations/literature-review-matrix-quantitative-research-review-type-columns.webp) The template above assumes a systematic review of quantitative studies, but the same skeleton flexes to other designs. The differences concentrate in which columns you add or replace. | Review type | Anchor reporting standard | Columns to add or change | |--|--|--| | Systematic review of quantitative studies | PRISMA 2020 [9][11] | Risk-of-bias tool and score, effect estimate, heterogeneity notes | | Meta-analysis | PRISMA plus the PECO or PICO protocol [5][12] | Pooled estimate, prediction or confidence interval, subgroup variables | | Systematic review without meta-analysis | PRISMA, narrative synthesis | Context, procedure, factor, outcome groupings in place of pooled estimates [3] | | Scoping review | Broad inclusion, mapping focus | Determinant category, population subtype, gap identified [6][2] | | Realist review | RAMESES guidelines [13] | Context, Mechanism, Outcome (CMO) hypotheses per source | The scoping review of HPV vaccination decision-making in Japan offers a clean illustration of a category column doing heavy work. It searched Medline, Embase, and Scopus from 2009 to 2025, then categorized every identified influence using the WHO Strategic Advisory Group of Experts on Immunisation vaccine hesitancy matrix, with contextual, individual and group, and vaccine-specific factors [6]. The "matrix" in that paper is a classification framework overlaid on the extraction table, which is a common and effective pattern: your literature review matrix supplies the rows, and an established conceptual matrix supplies the column categories. Realist reviews replace the effect-estimate column entirely. In the advance care planning review for people with multiple sclerosis, each article was tested against context, mechanism, and outcome hypotheses using abductive analysis [13]. If your question is about why an intervention works rather than how much, that is the variant you need. ## Quality Assessment Columns Every quantitative matrix needs at least one judgment column, and the tool name goes in the cell, not just the verdict. Published reviews match tools to study design. - **Joanna Briggs Institute (JBI) checklists** for experimental and observational laboratory and clinical studies. A tissue-graft review used the JBI checklist to assess study quality [8], and an enamel proteomics review used the revised JBI tool alongside the Quality Assessment Tool for In Vitro Studies (QUIN) [14]. - **Newcastle-Ottawa Scale (NOS)** for non-randomized human studies, used in the trace-element meta-analysis before pooling [5]. - **Quality in Prognosis Studies (QUIPS)** for prognostic factor research [3]. - **PRISMA for protocols.** If you register a protocol before searching, as the CBT-I economic evaluation protocol did across MEDLINE, PsycINFO, Cochrane, Scopus, CINAHL, Web of Science, and Embase, PRISMA-P governs the write-up [12]. Record two things per row: the tool with its version or checklist domain, and the resulting rating such as low, moderate, or high risk of bias. In the cartilage review this discipline paid off immediately, since 29 of 37 study-analysis units turned out to be at high risk of bias, which reframed how every downstream finding could be interpreted [3]. ## From Matrix to Manuscript: How to Write the Literature Review The matrix answers the question every student asks at this stage: how to write literature review prose that synthesizes rather than summarizes. The trick is to write from the columns, not from memory. **Organize the narrative by column patterns.** If you sort by effect direction, your first paragraph writes itself: across X of N studies the association ran one way, with exceptions concentrated in a particular design or population. If you sort by population or sample matrix, you get the structure used in the miRNA normalization review, which organized its synthesis around blood-derived sample types and the biological comparison being made [15]. **Report numbers once, in the paragraph where they carry the argument.** A review with a documented search protocol covering 94 marine or saline studies could report that solid-phase extraction appeared in 78 of them, and that only 10 studies reported both absolute recovery and a matrix-effect assessment [16]. Those two numbers, extraction method prevalence and a reporting gap, made the entire case for the paper's contribution in two sentences. That is the density you are aiming for. **Name the gap the matrix exposes.** Gaps are visible in a matrix and invisible in memory. The HP V vaccination scoping review identified research gaps directly from the categorized determinants [6]. The microplastic biomonitoring review found that research clustered geographically and by indicator species, which itself became a finding [10]. **Convert the Notes column into your limitations section.** Every caveat you recorded while extracting, from heterogeneous methodologies [9] to the impossibility of determining unique participants because of cohort overlap [3], is a sentence in your limitations paragraph waiting to be written. ## Common Mistakes and Limitations - **Copying abstracts into cells.** Abstract sentences pasted into the Key finding column produce a matrix you cannot synthesize from. Paraphrase in pass two, and keep paraphrases under twenty-five words. - **One row per paper regardless of outcomes.** Multivariate studies report many associations. If you collapse them into one row you lose the ability to see which factor-outcome pairs drive your conclusion [3]. - **Pooling incompatible effect measures.** Odds ratios, mean differences, and SMDs are not interchangeable. The cartilage reviewers refused meta-analysis for exactly this reason and synthesized narratively instead [3]. - **Ignoring quality columns when writing.** A consistent result across ten high-risk studies is weaker evidence than the same result in three low-risk studies. Weight your prose accordingly, and say so explicitly when most of your evidence base is weak. - **Ignoring preanalytical and matrix heterogeneity.** In measurement-focused questions, the sample type is a variable, not background detail. The miRNA normalization review found no universally applicable reference RNA, and concluded that candidate selection and the number of references must be justified for the intended comparison and sample matrix [15]. Build a sample-type column if your question touches measurement in biological material. - **Forgetting the search date stamp.** Undocumented searches cannot be reproduced or updated. Write the date and databases into your methods the day you run them. - **Confusing the matrix with the review.** A complete matrix is not a literature review. The written synthesis, the frameworks you derive, and the gaps you name are the product. The matrix is the scaffolding. - **Working without methodological guidance.** Study designs, quality tools, and reporting standards differ across fields, and individual projects need input from a supervisor, a librarian, or a statistician before you lock your protocol. ## Frequently Asked Questions ### What is a literature review matrix in quantitative research? It is a spreadsheet in which each row represents one study and each column captures a standardized field such as design, sample, effect estimate, and quality rating, so that quantitative evidence can be compared and synthesized systematically. ### How many columns should my template have? Twelve to sixteen columns covers most quantitative projects. Start with the core set in the table above, then add field-specific columns such as assay type or follow-up duration only when your question requires them. ### Is a literature review matrix required for a systematic review? A predefined data-extraction structure is expected in systematic reviews, and many teams implement it as a matrix, as published reviews that extracted data "following a predefined matrix" demonstrate [2]. ### How do I decide which studies go into the matrix? Only studies that meet the eligibility criteria from your PICO or PECO question, applied through documented screening of records from a systematic literature search with recorded databases and dates. ### Can I use a matrix for qualitative or mixed studies? Yes, but replace effect-estimate columns with theme, mechanism, or finding columns, as realist reviews do with context, mechanism, and outcome structures [13]. ### What software should I use for the matrix? Any spreadsheet works, including Excel, Google Sheets, and LibreOffice Calc. Teams that need reproducibility and shared screening often move to dedicated systematic review tools after the initial setup. ### How do I handle a study that reports several outcomes? Give the study one row per outcome or effect estimate, all sharing the same citation ID, and group them visually with a shared identifier so the rows stay linked during sorting. ### How does a matrix differ from a data extraction form? A data extraction form is filled once per study and stores everything, while a matrix is the comparative layer built from those extractions, trimmed to the columns you actually need for synthesis. ## Sources 1. [Middle managers' role in implementing evidence-based practices in healthcare: a systematic review.](https://pubmed.ncbi.nlm.nih.gov/30541596/) 2. [A scoping review on integrated health campaigns for immunization in low- and middle-income countries.](https://pubmed.ncbi.nlm.nih.gov/37699072/) 3. [Prognostic factors associated with outcomes after knee cartilage repair across different procedure types: a systematic review.](https://pubmed.ncbi.nlm.nih.gov/42902365/) 4. [Engineered Vascularization: Neurovascular Niche Remodeling Enables Biomimetic Brain Organoids for Central Nervous System Research.](https://pubmed.ncbi.nlm.nih.gov/42899767/) 5. [Trace element dysregulation in colorectal cancer: A systematic review and meta-analysis of ICP-based studies.](https://pubmed.ncbi.nlm.nih.gov/42976515/) 6. [Determinants of human papillomavirus vaccination decision-making in Japan: a scoping review exploring contextual, social, and adolescent-specific influences.](https://pubmed.ncbi.nlm.nih.gov/41412018/) 7. [Progress in the Mechanism of Heat Shock Proteins in Gastric Cancer: From Biomarkers to Therapeutic Targets.](https://pubmed.ncbi.nlm.nih.gov/42895230/) 8. [Comparative assessment of residual DNA quantification methods in human acellular dermal grafts: a systematic review.](https://pubmed.ncbi.nlm.nih.gov/42931963/) 9. [Morpho-molecular assessment of nanomaterials in teleost fish wound healing: a systematic review.](https://pubmed.ncbi.nlm.nih.gov/42859172/) 10. [Microplastic Biomonitoring: Global Distribution, Matrix Selection, and Intrinsic and Extrinsic Effects.](https://pubmed.ncbi.nlm.nih.gov/42859234/) 11. [Sumud as a framework for collective resilience: a systematic review of implications for transcultural psychiatry and psychotherapy.](https://pubmed.ncbi.nlm.nih.gov/42965091/) 12. [Economic evaluation of cognitive behavioural therapy for insomnia (CBT-I) for improving health outcomes in adult population: a systematic review protocol.](https://pubmed.ncbi.nlm.nih.gov/31699744/) 13. [A realist review of advance care planning for people with multiple sclerosis and their families.](https://pubmed.ncbi.nlm.nih.gov/33064749/) 14. [Proteomic Alterations in Hypomineralised Enamel: A Systematic Review.](https://pubmed.ncbi.nlm.nih.gov/42930447/) 15. [Normalization Strategies for miRNA RT-qPCR in Blood-Derived Samples in Obesity Research: Current Practices and Methodological Considerations.](https://pubmed.ncbi.nlm.nih.gov/42941853/) 16. [Analysis of pharmaceutical compounds under salinity constraints: From sorbent engineering to matrix-effect compensation.](https://pubmed.ncbi.nlm.nih.gov/43003724/)